Richard J. Povinelli

发表

Learning energy demand domain knowledge via feature transformation

Sanzad Siddique, Richard J. Povinelli, 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events

Richard J. Povinelli, Xin Feng, R. Povinelli, 2003, IEEE Trans. Knowl. Data Eng..

Richard J. Povinelli, George F. Corliss, Ronald H. Brown, 2015, 2015 IEEE Power & Energy Society General Meeting.

Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition

Richard J. Povinelli, Michael T. Johnson, Kevin M. Indrebo, 2005, NOLISP.

A Heart Cell Group Model for the Identification of Myocardial Ischemia

Richard J. Povinelli, Michael T. Johnson, Mohamed A. Mneimneh, 2008, HEALTHINF.

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Richard J. Povinelli, Ronald H. Brown, Gregory Merkel, 2018, Energies.

Time-domain isolated phoneme classification using reconstructed phase spaces

Richard J. Povinelli, Xiaolin Liu, Michael T. Johnson, 2005, IEEE Transactions on Speech and Audio Processing.

DATA MINING OF MULTIPLE NONSTATIONARY TIME SERIES

Richard J. Povinelli, Xin Feng, R. Povinelli, 1999 .

Identifying extreme cold events using phase space reconstruction

Richard J. Povinelli, George F. Corliss, Ronald H. Brown, 2016, Int. J. Appl. Pattern Recognit..

Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors

Richard J. Povinelli, Nabeel A. O. Demerdash, Aderiano M. da Silva, 2013, IEEE Transactions on Industrial Informatics.

Phoneme classification using naive Bayes classifier in reconstructed phase space

Richard J. Povinelli, Michael T. Johnson, Jinjin Ye, 2002, Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop..

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

Richard J. Povinelli, Xin Feng, R. Povinelli, 1999 .

A Novel Wireless System to Monitor Gait Using Smartshoe-Worn Sensors

Richard J. Povinelli, Sheikh Iqbal Ahamed, Roger O. Smith, 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.

Combined Conditional Random Fields and n -Gram Language Models for Gene Mention Recognition

Richard J. Povinelli, Michael T. Johnson, Jidong Tao, 2007 .

Smart topic detection for robot conversation

Richard J. Povinelli, Andrew B. Williams, Elise Russell, 2016, 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Deep Neural Network Regression for Short-Term Load Forecasting of Natural Gas

Richard J. Povinelli, Ronald H. Brown, Gregory Merkel, 2017 .

Time series data mining: identifying temporal patterns for characterization and prediction of time series events

Richard J. Povinelli, Xin Feng, R. Povinelli, 1999 .

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Henry Medeiros, Richard J. Povinelli, Anthony Hoak, 2017, Sensors.

Assessment of Pain Using Facial Pictures Taken with a Smartphone

Richard J. Povinelli, Sheikh Iqbal Ahamed, Golam Mushih Tanimul Ahsan, 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.

Book Review: Foundations of Genetic Programming

Richard J. Povinelli, 2004, Genetic Programming and Evolvable Machines.

Direct-to-Patient Survey for Diagnosis of Benign Paroxysmal Positional Vertigo

Richard J. Povinelli, Heidi Richburg, David Friedland, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

Deep Neural Network Regression as a Component of a Forecast Ensemble

Richard J. Povinelli, Ronald H. Brown, Gregory Merkel, 2017 .

Time series classification using Gaussian mixture models of reconstructed phase spaces

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2004, IEEE Transactions on Knowledge and Data Engineering.

Detecting determinism in speech phonemes

Richard J. Povinelli, Xiaolin Liu, Michael T. Johnson, 2002, Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop..

Minimum Mean-Squared Error Estimation of Mel-Frequency Cepstral Coefficients Using a Novel Distortion Model

Richard J. Povinelli, Michael T. Johnson, Kevin M. Indrebo, 2008, IEEE Transactions on Audio, Speech, and Language Processing.

Vowel classification by global dynamic modeling

Richard J. Povinelli, Xiaolin Liu, Michael T. Johnson, 2003, NOLISP.

Identification of ECG Arrhythmias Using Phase Space Reconstruction

Richard J. Povinelli, Kristina M. Ropella, Felice M. Roberts, 2001, PKDD.

Hierarchical Bayesian Data Fusion for Robotic Platform Navigation pdf

Henry Medeiros, Richard J. Povinelli, Yevgeniy Reznichenko, 2017, ArXiv.

Synergistic Use of Compound Properties and Docking Scores in Neural Network Modeling of CYP2D6 Binding: Predicting Affinity and Conformational Sampling

Richard J. Povinelli, Daniel S. Sem, Craig A. Struble, 2006, J. Chem. Inf. Model..

Data Improving in Time Series Using ARX and ANN Models

Richard J. Povinelli, Hermine N. Akouemo, R. Povinelli, 2017, IEEE Transactions on Power Systems.

Speech recognition using reconstructed phase space features

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Using Smart Meters to Learn Water Customer Behavior

Richard J. Povinelli, Michele R. B. Malinowski, 2020 .

A combined sub-band and reconstructed phase space approach to phoneme classification

Richard J. Povinelli, Michael T. Johnson, Kevin M. Indrebo, 2003, NOLISP.

IMPROVING GENETIC ALGORITHMS PERFORMANCE BY HASHING FITNESS VALUES

Richard J. Povinelli, Xin Feng, 1999 .

An ensemble model of QSAR tools for regulatory risk assessment

Richard J. Povinelli, Prachi Pradeep, Stephen J. Merrill, 2016, Journal of Cheminformatics.

MOESM1 of An ensemble model of QSAR tools for regulatory risk assessment

Richard J. Povinelli, Prachi Pradeep, Stephen J. Merrill, 2016 .

Probabilistic anomaly detection in natural gas time series data

Richard J. Povinelli, Hermine N. Akouemo, R. Povinelli, 2016 .

Improving Daily Natural Gas Forecasting by Tracking and Combining Models

Richard J. Povinelli, George F. Corliss, Ronald H. Brown, 2017 .

Phoneme classification over the reconstructed phase space using principal component analysis

Richard J. Povinelli, Michael T. Johnson, Jinjin Ye, 2003, NOLISP.

Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events

Richard J. Povinelli, R. Povinelli, 2000, TSDM.

Time series outlier detection and imputation

Richard J. Povinelli, Hermine N. Akouemo, R. Povinelli, 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

A light weight smartphone based human activity recognition system with high accuracy

Richard J. Povinelli, Md. Osman Gani, Roger O. Smith, 2019, J. Netw. Comput. Appl..

Statistical models of reconstructed phase spaces for signal classification

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2006, IEEE Transactions on Signal Processing.

Study of attractor variation in the reconstructed phase space of speech signals

Richard J. Povinelli, Michael T. Johnson, Jinjin Ye, 2003, NOLISP.

Sub-banded reconstructed phase spaces for speech recognition

Richard J. Povinelli, Michael T. Johnson, Kevin M. Indrebo, 2006, Speech Commun..

Searching for non-sense: identification of pacemaker non-sense and non-capture failures using machine learning techniques

Richard J. Povinelli, M.R.B. Malinowski, 2003, Computers in Cardiology, 2003.

Diagnostics of Faults in Induction Motor ASDs Using Time-Stepping Coupled Finite Element State-Space and Time Series Data Mining Techniques

Richard J. Povinelli, Nabeel A. O. Demerdash, Ronald H. Brown, 2000 .

Joint frequency domain and reconstructed phase space features for speech recognition

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Nonlinear Modeling: Genetic Programming vs. Fast Evolutionary Programming

Richard J. Povinelli, Minglei Duan, R. Povinelli, 2001 .

Improving Computational Performance of Genetic Algorithms: A Comparison of Techniques

Richard J. Povinelli, 2000 .

Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes

Richard J. Povinelli, Nabeel A. O. Demerdash, Aderiano M. da Silva, 2008, IEEE Transactions on Industrial Electronics.

Speech Recognition Using Features Extracted from Phase Space Reconstructions

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2003 .

Performance of nonlinear speech enhancement using phase space reconstruction

Richard J. Povinelli, Michael T. Johnson, Andrew C. Lindgren, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

Richard J. Povinelli, Chi Nok Enoch Kan, Dong Hye Ye, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Are nonlinear ventricular arrhythmia characteristics lost, as signal duration decreases?

Richard J. Povinelli, Kristina M. Ropella, Michael T. Johnson, 2002, Computers in Cardiology.

Using Genetic Algorithms To Find Temporal Patterns Indicative Of Time Series Events

Richard J. Povinelli, 2000 .

A reconstructed phase space approach for distinguishing ischemic from non-ischemic ST changes using Holter ECG data

Richard J. Povinelli, Kristina M. Ropella, Michael T. Johnson, 2003, Computers in Cardiology, 2003.

A Temporal Pattern Approach for Predicting Weekly Financial Time Series

Richard J. Povinelli, David H. Diggs, 2003 .

Generalized phase space projection for nonlinear noise reduction

Richard J. Povinelli, Michael T. Johnson, R. Povinelli, 2005 .

Synergistic Use of Compound Properties and Docking Scores in Neural Network Modeling of CYP2D6 Binding: Predicting Affinity and Conformational Sampling.

Richard J. Povinelli, Daniel S. Sem, Craig A. Struble, 2007 .